Testing weak form informational efficiency on the Tunisian stock market using long memory models

Testing weak form informational efficiency on the Tunisian stock market using long memory models

Mohamed Ali HOUFI

Department of Finance and Investment, Faculty of Business Administration, University of Tabuk, KSA.

American Journal of Financial Management

The purpose of this paper is to test the weak-form market efficiency of the Tunisian stock market using recent developments in time series econometrics. The efficiency hypothesis was tested by using the class of long memory models namely ARFIMA-FIGARCH. For this, we will attempt to examine the long memory behavior in the returns and the volatility series of the Tunisian stock market index namely Tunindex. Our empirical study covers a sample covering the Tunindex during the period: 02/01/1998 to 16/03/2018. Our results show the presence of the long memory property in the return and volatility specified respectively by an ARFIMA and FIGARCH process. This result implies that it is possible to predict future stock prices and an extraordinary gain could be obtained when trading in this market, which displays that the Tunisian stock market is not efficient in its weak-form.

Keywords:  long memory, ARFIMA, FIEGARCH, heteroscedasticity, efficiency.

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How to cite this article:
Mohamed Ali HOUFI. Testing weak form informational efficiency on the Tunisian stock market using long memory models. American Journal of Financial Management, 2019, 2:6. DOI: 10.28933/ajfm-2019-09-1305


1. Baillie et al. (1996). “fractionally Integrated Generalized Autoregressive Conditional Heteroscedasticity, Journal of Econometrics, 74, 3-30.
2. Bollerslev T. (1986). “Generalized Autoregressive Conditional Heteroscedasticity”, Journal of Econometrics, 31: p. 307-327.
3. Bollerslev T. (1987). “A Conditional Heteroskedastic Time Series Model for Speculative Prices and Rates of Return”, The Review of Economics and Statistics, 69: p. 542-547.
4. Bollerslev T. (1988). “On the Correlation Structure for the Generalized Autoregressive Conditional Heteroskedastic Process”, Journal of Time Series Analysis 9, 121-131.
5. Bollerslev et al. (1996). “Modeling and pricing long memory in stock market volatility”, Journal of Econometrics, 73, 151-184.
6. Bollerslev et al. (1986). “Modeling the Persistence of Conditional Variances (with discussion),” Econometric Reviews 5, 1-50.
7. Bollerslev et al. (1993). “Common Persistence in Conditional Variance”, Econometrica 61, 166-187.
8. Bollerslev et al. (1992). “Quasi-Maximum Likelihood Estimation and Inference in Dynamic Models with Time-Varying Covariances” Econometric Reviews, 11 (2): pp. 143-172.
9. BOUTAHAR et al. (2006). “Estimation methods of the long memory parameter: Monte Carlo analysis and application”, Journal of Applied Statistic 34 (3), p. 261-301.
10. Diebold et al. (1989). “Long Memory and Persistence in Aggregate Output”, Journal of Monetary Economics, 24, pp. 189-209.
11. DING Z et al. (1996). “Modeling Volatility Persistence of Speculative Returns: A New Approach”, Journal of Econometrics, 73, p. 185-215.
12. Engle. (1982). “Autoregressive Conditional Heteroscedasticity with Estimates of the variance of United Kingdom Infl ation”, Econometrica, 50 (4): p. 987-1008.
13. Engle et al. (1986). “Modeling the persistence of conditional variances,” Econometric Reviews, 5, p. 1-50.
14. FAMA. (1970). “Efficient Capital Markets: A Review of Theory and Empirical Work”. Journal of Finance, 25, 383-417.
15. FAMA. (1965). “Random Walks in Stock Market Prices”, Financial Analysts Journal, 21, No. 5, p. 55-59.
16. GRANGER et al. (1980). “An Introduction to Long-Memory Time Series Models and Fractional Differencing” Journal of Time Series Analysis, 1, No. 1, pp. 15-29.
17. LARDIC et al. (1997). “ position to test the degree of long series memory. The example of ARFIMA modeling, “Applied Economics, No. 2, pp. 161-195.
18. LARDIC et al. (1999). “ARFIMA forecast of exchange rates: modelers should they still urge the naive forecast?”, Annals of Economics and Statistics, 54 n.
19. LAWRENCE et al. (1999). “the contribution of long memory periodic models for modeling of day effect on the volatility of financial series “University of Liège.
20. Lee et al. (1994). “ Asymptotic Theory For the GARCH (1, l) Quasi-Maximum Likelihood Estimator, Econometric Theory, 10, 29-52.
21. LO. (1991). “Long-Term Memory in Stock Market Prices”, Econometrica, 59, pp. 1279-1313.
22. Mandelbrot. (1963). “The variation of some speculative prices. The Journal of Business, 36 (4): 394-419.
23. NELSON. (1991). “ Conditional heteroscedasticity in asset returns: A new approach, Econometrica, 59, 2, 347-370, March.
24. SOWELL. (1992). “Modeling Long-Run Behavior with the Fractional ARIMA Model,” Journal of Monetary Economics, 29, pp. 277-302.
25. SUMMERS. (1986). “Does the Stock Market rationally Reflect Fundamental Values? “The Journal of Finance, Vol. XLI, No. 3, p. 591-601.